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Most datasets in the field of document analysis utilize highly standardized labels, which, while simplifying specific tasks, often produce outputs that are not directly applicable to humanities research. In contrast, the Nuremberg Letterbooks dataset, which comprises historical documents from the early 15th century, addresses this gap by providing multiple types of transcriptions and accompanying metadata. This approach allows for developing methods that are more closely aligned with the needs of the humanities. The dataset includes 4 books containing 1711 labeled pages written by 10 scribes. Three types of transcriptions are provided for handwritten text recognition: Basic, diplomatic, and regularized. For the latter two, versions with and without expanded abbreviations are also available. A combination of letter ID and writer ID supports writer identification due to changing writers within pages. In the technical validation, we established baselines for various tasks, demonstrating data consistency and providing benchmarks for future research to build upon.

MICADO is a first-generation instrument for the ELT. It will provide diffraction-limited imaging in standard, astrometric, and coronagraphic modes and long-slit spectroscopy at near-infrared wavelengths. The core of the MICADO instrument is its cryostat, which cools the internal optical and mechanical subsystems to 80 K. Following a light ray entering the cryostat through the entrance window, the first mechanism it encounters is the Focal Plane Mechanism. It consists of two independent movable devices mounted in one assembly: the aperture wheel and the focal plane wheel. The primary purpose of the aperture wheel is to rapidly block the light path, which is needed to mitigate persistence on the detectors. The focal plane wheel holds field stops, calibration masks, slits, and coronagraphs. The positioning requirements for the wheel are dominated by the coronographic masks demanding a 15 $\mu$m RMS repeatability. To fulfill this specification and avoid mechanical wear in the drive, a novel magnetically coupled gear system was developed at the Max Planck Institute for Extraterrestrial Physics (MPE). A magnetically coupled worm gear uses magnetic forces to transmit torque from the motor to the driven component without direct mechanical contact. This paper describes the design and performance of the magnetic drive and the first results of the focal plane wheel prototype tests in a cryogenic environment., Comment: Proceeding of SPIE Astronomical Telescopes + Instrumentation 2024

MICADO is a first light instrument for the Extremely Large Telescope (ELT), set to start operating later this decade. It will provide diffraction limited imaging, astrometry, high contrast imaging, and long slit spectroscopy at near-infrared wavelengths. During the initial phase operations, adaptive optics (AO) correction will be provided by its own natural guide star wavefront sensor. In its final configuration, that AO system will be retained and complemented by the laser guide star multi-conjugate adaptive optics module MORFEO (formerly known as MAORY). Among many other things, MICADO will study exoplanets, distant galaxies and stars, and investigate black holes, such as Sagittarius A* at the centre of the Milky Way. After their final design phase, most components of MICADO have moved on to the manufacturing and assembly phase. Here we summarize the final design of the instrument and provide an overview about its current manufacturing status and the timeline. Some lessons learned from the final design review process will be presented in order to help future instrumentation projects to cope with the challenges arising from the substantial differences between projects for 8-10m class telescopes (e.g. ESO-VLT) and the next generation Extremely Large Telescopes (e.g. ESO-ELT). Finally, the expected performance will be discussed in the context of the current landscape of astronomical observatories and instruments. For instance, MICADO will have similar sensitivity as the James Webb Space Telescope (JWST), but with six times the spatial resolution., Comment: Proceedings of the SPIE, Volume 13096, id. 1309611 11 pp. (2024)

Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning, and sensing. However, efficient and reliable edge learning remains difficult with current technology due to the lack of personalized data, insufficient hardware capabilities, and inherent challenges posed by online learning. Over time and across multiple developmental stages, the brain has evolved to efficiently incorporate new knowledge by gradually building on previous knowledge. In this work, we emulate the multiple stages of learning with digital neuromorphic technology that simulates the neural and synaptic processes of the brain using two stages of learning. First, a meta-training stage trains the hyperparameters of synaptic plasticity for one-shot learning using a differentiable simulation of the neuromorphic hardware. This meta-training process refines a hardware local three-factor synaptic plasticity rule and its associated hyperparameters to align with the trained task domain. In a subsequent deployment stage, these optimized hyperparameters enable fast, data-efficient, and accurate learning of new classes. We demonstrate our approach using event-driven vision sensor data and the Intel Loihi neuromorphic processor with its plasticity dynamics, achieving real-time one-shot learning of new classes that is vastly improved over transfer learning. Our methodology can be deployed with arbitrary plasticity models and can be applied to situations demanding quick learning and adaptation at the edge, such as navigating unfamiliar environments or learning unexpected categories of data through user engagement., Comment: 17 page journal article. Submitted to IOP NCE

Stacking faults (SF) are important structural defects that play an essential role in the deformation of engineering alloys. However, direct observation of stacking faults at the atomic scale can be challenging. Here, we use the analytical field ion microscopy (aFIM), including density-functional theory informed contrast estimation, to image local elemental segregation at SFs in a creep-deformed solid solution single crystal alloy of Ni-2 at.% W. The segregated atoms are imaged brightly, and time-of-flight spectrometry allows for their identification as W. We also provide the first quantitative analysis of trajectory aberration, with a deviation of approximately 0.4 nm, explaining why atom probe tomography could not resolve these segregations. Atomistic simulations of substitutional W atoms at an edge dislocation in fcc Ni using an analytic bond-order potential indicate that the experimentally observed segregation is due to the energetic preference of W for the center of the stacking fault, contrasting with e.g., Re segregating to partial dislocations. Solute segregation to SF can hinder dislocation motion, increasing the strength of Ni-based superalloys. Yet direct substitution of Re by W envisaged to lower superalloys' costs, requires extra consideration in alloy design since these two solutes do not have comparable interactions with structural defects during deformation.

This paper presents an efficient algorithm for the approximation of the rank-one convex hull in the context of nonlinear solid mechanics. It is based on hierarchical rank-one sequences and simultaneously provides first and second derivative information essential for the calculation of mechanical stresses and the computational minimization of discretized energies. For materials, whose microstructure can be well approximated in terms of laminates and where each laminate stage achieves energetic optimality with respect to the current stage, the approximate envelope coincides with the rank-one convex envelope. Although the proposed method provides only an upper bound for the rank-one convex hull, a careful examination of the resulting constraints shows a decent applicability in mechanical problems. Various aspects of the algorithm are discussed, including the restoration of rotational invariance, microstructure reconstruction, comparisons with other semi-convexification algorithms, and mesh independency. Overall, this paper demonstrates the efficiency of the algorithm for both, well-established mathematical benchmark problems as well as nonconvex isotropic finite-strain continuum damage models in two and three dimensions. Thereby, for the first time, a feasible concurrent numerical relaxation is established for an incremental, dissipative large-strain model with relevant applications in engineering problems.

This work introduces the conditioned Vehicle Motion Diffusion (cVMD) model, a novel network architecture for highway trajectory prediction using diffusion models. The proposed model ensures the drivability of the predicted trajectory by integrating non-holonomic motion constraints and physical constraints into the generative prediction module. Central to the architecture of cVMD is its capacity to perform uncertainty quantification, a feature that is crucial in safety-critical applications. By integrating the quantified uncertainty into the prediction process, the cVMD's trajectory prediction performance is improved considerably. The model's performance was evaluated using the publicly available highD dataset. Experiments show that the proposed architecture achieves competitive trajectory prediction accuracy compared to state-of-the-art models, while providing guaranteed drivable trajectories and uncertainty quantification., Comment: Accepted at IEEE/CVF Computer Vision and Pattern Recognition Conference Workshops (CVPRW) 2024

This work provides a comprehensive analysis and interpretation of the graph spectral representation of traffic scenarios. Based on a spatio-temporal vehicle interaction graph, an observed traffic scenario can be transformed into the graph spectral domain by means of the multidimensional Graph Fourier Transformation. Since these spectral scenario representations have shown to successfully incorporate the complex and interactive nature of traffic scenarios, the beneficial feature representation is employed for the purpose of predicting vehicle trajectories. This work introduces GFTNNv2, a deep learning network predicting vehicle trajectories in the graph spectral domain. Evaluation of the GFTNNv2 on the publicly available datasets highD and NGSIM shows a performance gain of up to 25% in comparison to state-of-the-art prediction approaches., Comment: Accepted as a conference paper for IEEE ITSC 2023, Bilbao, Spain

Based on the characterization of the polyconvex envelope of isotropic functions by their signed singular value representations, we propose a simple algorithm for the numerical approximation of the polyconvex envelope. Instead of operating on the $d^2$-dimensional space of matrices, the algorithm requires only the computation of the convex envelope of a function on a $d$-dimensional manifold, which is easily realized by standard algorithms. The significant speedup associated with the dimensional reduction from $d^2$ to $d$ is demonstrated in a series of numerical experiments., Comment: 17 pages, 7 figures

For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and sparse graph structures a proper optimization is problematic. To surpass limitations, this work proposes architectural modifications of GATv2. In controlled experiments, it is shown that the proposed model adaptions improve prediction performance in a node-level regression task and make it more robust to parameter initialization. This work aims for a better understanding of the attention mechanism and analyzes its interpretability of identifying causal importance., Comment: Accepted as a conference paper in IEEE IV 2023, Anchorage, Alaska, USA

This work introduces the multidimensional Graph Fourier Transformation Neural Network (GFTNN) for long-term trajectory predictions on highways. Similar to Graph Neural Networks (GNNs), the GFTNN is a novel network architecture that operates on graph structures. While several GNNs lack discriminative power due to suboptimal aggregation schemes, the proposed model aggregates scenario properties through a powerful operation: the multidimensional Graph Fourier Transformation (GFT). The spatio-temporal vehicle interaction graph of a scenario is converted into a spectral scenario representation using the GFT. This beneficial representation is input to the prediction framework composed of a neural network and a descriptive decoder. Even though the proposed GFTNN does not include any recurrent element, it outperforms state-of-the-art models in the task of highway trajectory prediction. For experiments and evaluation, the publicly available datasets highD and NGSIM are used, Comment: Accepted as a conference paper in ITSC 2022, Macau, China

This work provides a comprehensive derivation of the parameter gradients for GATv2 [4], a widely used implementation of Graph Attention Networks (GATs). GATs have proven to be powerful frameworks for processing graph-structured data and, hence, have been used in a range of applications. However, the achieved performance by these attempts has been found to be inconsistent across different datasets and the reasons for this remains an open research question. As the gradient flow provides valuable insights into the training dynamics of statistically learning models, this work obtains the gradients for the trainable model parameters of GATv2. The gradient derivations supplement the efforts of [2], where potential pitfalls of GATv2 are investigated.

This paper presents computationally feasible rank-one relaxation algorithms for the efficient simulation of a time-incremental damage model with nonconvex incremental stress potentials in multiple spatial dimensions. While the standard model suffers from numerical issues due to the lack of convexity, the relaxation by rank-one convexification prevents non-existence of minimizers and mesh dependence of the solutions of finite element discretizations. By the combination, modification and parallelization of the underlying convexification algorithms, the novel approach becomes computationally feasible. A descent method and a Newton scheme enhanced by step-size control prevent stability issues related to local minima in the energy landscape and the computation of derivatives. Numerical techniques for the construction of continuous derivatives of the approximated rank-one convex envelope are discussed. A series of numerical experiments demonstrates the ability of the computationally relaxed model to capture softening effects and the mesh independence of the computed approximations. An interpretation in terms of microstructural damage evolution is given, based on the rank-one lamination process.

We introduce and theoretically analyze a scheme to prepare and detect non-Gaussian quantum states of an optically levitated particle via the interaction with a light pulse that generates cubic and inverted potentials. We show that this allows operating on short time- and lengthscales, which significantly reduces the demands on decoherence rates in such experiments. Specifically, our scheme predicts the observation of interference of nanoparticles with a mass above $10^8$ atomic mass units delocalised over several nanometers, on timescales of milliseconds, when operated at vacuum levels around $10^{-10}$~mbar and at room temperature. We discuss the prospect of using this approach for coherently splitting the wavepacket of massive dielectric objects using neither projective measurements nor an internal level structure., Comment: revtex 4-2. Main: 5 pages, 4 figures. Supplementary: 18 pages, 6 figures

Co-base superalloys are considered as promising high temperature materials besides the well-established Ni-base superalloys. However, Ni appears to be an indispensable alloying element also in Co-base superalloys. To address the influence of the base elements on the deformation behavior, high-temperature compressive creep experiments were performed on a single crystal alloy series that was designed to exhibit a varying Co/Ni ratio and a constant Al, W and Cr content. Creep tests were performed at 900 {\deg}C and 250 MPa and the resulting microstructures and defect configurations were characterized via electron microscopy. The minimum creep rates differ by more than one order of magnitude with changing Co/Ni ratio. An intermediate CoNi-base alloy exhibits the overall highest creep strength. Several strengthening contributions like solid solution strengthening of the $\gamma$ phase, effective diffusion coefficients or stacking fault energies were quantified. Precipitate shearing mechanisms differ significantly when the base element content is varied. While the Ni-rich superalloys exhibit SISF and SESF shearing, the Co-rich alloys develop extended APBs when the $\gamma^\prime$ phase is cut. This is mainly attributed to a difference in planar fault energies, caused by a changing segregation behavior. As result, it is assumed that the shearing resistivity and the occurring deformation mechanisms in the $\gamma^\prime$ phase are crucial for the creep properties of the investigated alloy series.

A systematic study of the compression creep properties of a single-crystalline Co-base superalloy (Co-9Al-7.5W-2Ta) was conducted at 950 {\deg}C, 975 {\deg}C and 1000 {\deg}C to reveal the influence of temperature and the resulting diffusion velocity of solutes like Al, W and Ta on the deformation mechanisms. Two creep rate minima are observed at all temperatures indicating that the deformation mechanisms causing these minima are quite similar. Atom-probe tomography analysis reveals elemental segregation to stacking faults, which had formed in the $\gamma\prime$ phase during creep. Density-functional-theory calculations indicate segregation of W and Ta to the stacking fault and an associated considerable reduction of the stacking fault energy. Since solutes diffuse faster at a higher temperature, segregation can take place more quickly. This results in a significantly faster softening of the alloy, since cutting of the $\gamma\prime$ precipitate phase by partial dislocations is facilitated through segregation already during the early stages of creep. This is confirmed by transmission electron microscopy analysis. Therefore, not only the smaller precipitate fraction at higher temperatures is responsible for the worse creep properties, but also faster diffusion-assisted shearing of the $\gamma\prime$ phase by partial dislocations. The understanding of these mechanisms will help in future alloy development by offering new design criteria.

This paper introduces the Descriptive Variational Autoencoder (DVAE), an unsupervised and end-to-end trainable neural network for predicting vehicle trajectories that provides partial interpretability. The novel approach is based on the architecture and objective of common variational autoencoders. By introducing expert knowledge within the decoder part of the autoencoder, the encoder learns to extract latent parameters that provide a graspable meaning in human terms. Such an interpretable latent space enables the validation by expert defined rule sets. The evaluation of the DVAE is performed using the publicly available highD dataset for highway traffic scenarios. In comparison to a conventional variational autoencoder with equivalent complexity, the proposed model provides a similar prediction accuracy but with the great advantage of having an interpretable latent space. For crucial decision making and assessing trustworthiness of a prediction this property is highly desirable., Comment: Accepted for IEEE International Conference on Intelligent Transportation Systems (ITSC 2021)

Imaging individual vacancies in solids and revealing their interactions with solute atoms remains one of the frontiers in microscopy and microanalysis. Here we study a creep-deformed binary Ni-2 at.% Ta alloy. Atom probe tomography reveals a random distribution of Ta. Field ion microscopy, with contrast interpretation supported by density-functional theory and time-of-flight mass spectrometry, evidences a positive correlation of tantalum with vacancies. Our results support solute-vacancy binding, which explains improvement in creep resistance of Ta-containing Ni-based superalloys and helps guide future material design strategies., Comment: Submitted to Physics Review Letter

Cycling processes are important in many areas of physics ranging from lasers to topological insulators, often offering surprising insights into dynamical and structural aspects of the respective system. Here we report on a quantum-nonlinear wave-mixing experiment where resonant lasers and an optical cavity define a closed cycle between several ground and excited states of a single atom. We show that, for strong atom-cavity coupling and steady-state driving, the entanglement between the atomic states and intracavity photon number suppresses the excited-state population via quantum interference, effectively reducing the cycle to the atomic ground states. The system dynamics then result from transitions within a harmonic ladder of entangled dark states, one for each cavity photon number, and a quantum Zeno blockade that generates antibunching in the photons emitted from the cavity. The reduced cycle suppresses unwanted optical pumping into atomic states outside the cycle, thereby enhancing the number of emitted photons.